Different patterns of bacterial communities have been reported in the airways and gastrointestinal tract of asthmatics when compared to healthy controls. However, the blood microbiome of asthmatics is yet to be investigated. Therefore, we aimed to determine whether a distinct serum microbiome is observed in asthmatics by metagenomic analysis of serum extracellular vesicles (EVs). We obtained serum from 190 adults with asthma and 260 healthy controls, from which EVs were isolated and analyzed. The bacterial composition of asthmatics was significantly different from that of healthy controls. Chao 1 index was significantly higher in the asthma group, while Shannon and Simpson indices were higher in the control group. At the phylum level, Bacteroidetes was more abundant in asthmatics, while Actinobacter, Verrucomicrobia, and Cyanobacteria were more abundant in healthy controls. At the genus level, 24 bacterial genera showed differences in relative abundance between asthmatics and controls, with linear discriminant analysis scores greater than 3. Further, in a diagnostic model based on these differences, a high predictive value with a sensitivity of 0.92 and a specificity of 0.93 was observed. In conclusion, we demonstrated distinct blood microbiome in asthma indicating the role of microbiome as a potential diagnostic marker of asthma.When making important decisions such as choosing health insurance or a school, people are often uncertain what levels of attributes will suit their true preference. After choice, they might realize that their uncertainty resulted in a mismatch choosing a sub-optimal alternative, while another available alternative better matches their needs. We study here the overall impact, from a central planner's perspective, of decisions under such uncertainty. We use the representation of Voronoi tessellations to locate all individuals and alternatives in an attribute space. We provide an expression for the probability of correct match, and calculate, analytically and numerically, the average percentage of matches. We test dependence on the level of uncertainty and location. We find that the overall mismatch is considerable even for low uncertainty-a possible concern for policy makers. We further explore a commonly used practice-allocating service representatives to assist individuals' decisions. We show that within a given budget and uncertainty level, the effective allocation is for individuals who are close to the boundary between several Voronoi cells, but are not right on the boundary.McrA is a key transcription factor that functions as a global repressor of fungal secondary metabolism in Aspergillus species. Here, we report that mcrA is one of the VosA-VelB target genes and McrA governs the cellular and metabolic development in Aspergillus nidulans. The deletion of mcrA resulted in a reduced number of conidia and decreased mRNA levels of brlA, the key asexual developmental activator. In addition, the absence of mcrA led to a loss of long-term viability of asexual spores (conidia), which is likely associated with the lack of conidial trehalose and increased β-(1,3)-glucan levels in conidia. In supporting its repressive role, the mcrA deletion mutant conidia contain more amounts of sterigmatocystin and an unknown metabolite than the wild type conidia. While overexpression of mcrA caused the fluffy-autolytic phenotype coupled with accelerated cell death, deletion of mcrA did not fully suppress the developmental defects caused by the lack of the regulator of G-protein signaling protein FlbA. On the contrary to the cellular development, sterigmatocystin production was restored in the ΔflbA ΔmcrA double mutant, and overexpression of mcrA completely blocked the production of sterigmatocystin. Overall, McrA plays a multiple role in governing growth, development, spore viability, and secondary metabolism in A. nidulans.This study investigates the use of microalgae as a biosorbent to eliminate heavy metals ions from wastewater. The Chlorella kessleri microalgae species was employed to biosorb heavy metals from synthetic wastewater specimens. https://www.selleckchem.com/products/ABT-263.html FTIR, and SEM/XRD analyses were utilized to characterize the microalgal biomass (the adsorbent). The experiments were conducted with several process parameters, including initial solution pH, temperature, and microalgae biomass dose. In order to secure the best experimental conditions, the optimum parameters were estimated using an integrated response surface methodology (RSM), desirability function (DF), and crow search algorithm (CSA) modeling approach. A maximum lead(II) removal efficiency of 99.54% was identified by the RSM-DF platform with the following optimal set of parameters pH of 6.34, temperature of 27.71 °C, and biomass dosage of 1.5 g L-1. The hybrid RSM-CSA approach provided a globally optimal solution that was similar to the results obtained by the RSM-DF approach. The consistency of the model-predicted optimum conditions was confirmed by conducting experiments under those conditions. It was found that the experimental removal efficiency (97.1%) under optimum conditions was very close (less than a 5% error) to the model-predicted value. The lead(II) biosorption process was better demonstrated by the pseudo-second order kinetic model. Finally, simultaneous removal of metals from wastewater samples containing a mixture of multiple heavy metals was investigated. The removal efficiency of each heavy metal was found to be in the following order Pb(II)?&gt;?Co(II)?&gt;?Cu(II)?&gt;?Cd(II)?&gt;?Cr(II).Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detection technique to recognize NPA, we proposed an automatic detection method of NPA on fundus fluorescein angiography (FFA) in DME. The study included 3,014 FFA images of 221 patients with DME. We use 3 convolutional neural networks (CNNs), including DenseNet, ResNet50, and VGG16, to identify non-perfusion regions (NP), microaneurysms, and leakages in FFA images. The NPA was segmented using attention U-net. To validate its performance, we applied our detection algorithm on 249 FFA images in which the NPA areas were manually delineated by 3 ophthalmologists. For DR lesion classification, area under the curve is 0.8855 for NP regions, 0.9782 for microaneurysms, and 0.9765 for leakage classifier. The average precision of NP region overlap ratio is 0.643. NP regions of DME in FFA images are identified based a new automated deep learning algorithm. This study is an in-depth study from computer-aided diagnosis to treatment, and will be the theoretical basis for the application of intelligent guided laser.